library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
Data Source https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960825/
Mohino-Herranz I, Gil-Pita R, Rosa-Zurera M, Seoane F. Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study. Sensors (Basel). 2019 Dec 13;19(24):0. doi: 10.3390/s19245524. PMID: 31847261; PMCID: PMC6960825.
Activitydata <- read.csv("~/GitHub/LatentBiomarkers/Data/ActivityData/data.txt", header=FALSE, stringsAsFactors=TRUE)
featNames <- read.table("~/GitHub/LatentBiomarkers/Data/ActivityData/Featurelabels.txt", quote="\"", comment.char="")
featNames <- as.character(t(featNames))
featNames <- str_replace_all(featNames,"\\(abs\\)","_A_")
featNames[duplicated(featNames)] <- paste(featNames[duplicated(featNames)],"D",sep="_")
rep_ID <- numeric(nrow(Activitydata))
ctr <- 1
for (ind in c(1:(nrow(Activitydata)-1)))
{
rep_ID[ind] <- ctr
if (Activitydata$V1[ind] != Activitydata$V1[ind+1]) ctr <- 0;
ctr <- ctr + 1
}
rownames(Activitydata) <- paste(Activitydata$V1,rep_ID,sep="_")
colnames(Activitydata) <- c("ID",featNames,"class")
Activitydata$rep <- rep_ID
tb <- table(Activitydata$class)
classes <- c("Neu","Emo","Men","Phy")
names(classes) <- names(tb)
Activitydata$class <- classes[as.character(Activitydata$class)]
table(Activitydata$class)
#>
#> Emo Men Neu Phy
#> 1120 1120 1120 1120
ID_class <- paste(Activitydata$ID,Activitydata$class)
IDCLASS <- unique(ID_class)
theclass <- Activitydata$class[!duplicated(ID_class)]
theIDs <- Activitydata$ID[!duplicated(ID_class)]
ActivitydataAvg <- NULL
for (id in IDCLASS)
{
ActivitydataAvg <- rbind(ActivitydataAvg,apply(Activitydata[ID_class==id,featNames],2,mean))
}
colnames(ActivitydataAvg) <- featNames
rownames(ActivitydataAvg) <- IDCLASS
ActivitydataAvg <- as.data.frame(ActivitydataAvg)
ActivitydataAvg$class <- theclass
ActivitydataAvg$ID <- theIDs
table(ActivitydataAvg$class)
#>
#> Emo Men Neu Phy
#> 40 40 40 40
ActivitydataAvg <- subset(ActivitydataAvg, class=="Men" | class=="Emo")
ActivitydataAvg$class <- 1*(ActivitydataAvg$class == "Men")
table(ActivitydataAvg$class)
#>
#> 0 1
#> 40 40
studyName <- "Activity"
dataframe <- ActivitydataAvg
outcome <- "class"
TopVariables <- 10
thro <- 0.80
cexheat = 0.15
Some libraries
library(psych)
library(whitening)
library("vioplot")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 80 | 534 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 40 | 40 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1000
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
dataframe <- FRESAScale(dataframe,method="OrderLogit")$scaledData
if (!largeSet)
{
hm <- heatMaps(data=dataframe,
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
1
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 362 , Uni p: 0.01628202 , Uncorrelated Base: 73 , Outcome-Driven Size: 0 , Base Size: 73
#>
#>
1 <R=1.000,w= 1,N= 265>, Top: 49( 21 )[ 1 : 49 : 0.975 ]( 48 , 185 , 0 ),<|>Tot Used: 233 , Added: 185 , Zero Std: 0 , Max Cor: 1.000
#>
2 <R=1.000,w= 1,N= 265>, Top: 26( 6 )[ 1 : 26 : 0.975 ]( 26 , 93 , 48 ),<|>Tot Used: 258 , Added: 93 , Zero Std: 0 , Max Cor: 1.000
#>
3 <R=1.000,w= 1,N= 265>, Top: 11( 2 )[ 1 : 11 : 0.975 ]( 10 , 18 , 74 ),<|>Tot Used: 258 , Added: 18 , Zero Std: 0 , Max Cor: 1.000
#>
4 <R=1.000,w= 2,N= 92>, Top: 35( 1 )[ 1 : 35 : 0.950 ]( 35 , 47 , 84 ),<|>Tot Used: 279 , Added: 47 , Zero Std: 0 , Max Cor: 0.993
#>
5 <R=0.993,w= 2,N= 92>, Top: 11( 1 )[ 1 : 11 : 0.947 ]( 11 , 11 , 101 ),<|>Tot Used: 280 , Added: 11 , Zero Std: 0 , Max Cor: 0.946
#>
6 <R=0.946,w= 2,N= 92>, Top: 19( 1 )[ 1 : 19 : 0.923 ]( 18 , 19 , 109 ),<|>Tot Used: 290 , Added: 19 , Zero Std: 0 , Max Cor: 0.943
#>
7 <R=0.943,w= 2,N= 92>, Top: 3( 1 )[ 1 : 3 : 0.921 ]( 3 , 3 , 114 ),<|>Tot Used: 292 , Added: 3 , Zero Std: 0 , Max Cor: 0.920
#>
8 <R=0.920,w= 3,N= 102>, Top: 44( 1 )[ 1 : 44 : 0.860 ]( 43 , 49 , 117 ),<|>Tot Used: 309 , Added: 49 , Zero Std: 0 , Max Cor: 0.983
#>
9 <R=0.983,w= 3,N= 102>, Top: 5( 1 )[ 1 : 5 : 0.892 ]( 5 , 6 , 135 ),<|>Tot Used: 309 , Added: 6 , Zero Std: 0 , Max Cor: 0.957
#>
10 <R=0.957,w= 4,N= 44>, Top: 21( 1 )[ 1 : 21 : 0.829 ]( 20 , 22 , 136 ),<|>Tot Used: 313 , Added: 22 , Zero Std: 0 , Max Cor: 0.892
#>
11 <R=0.892,w= 4,N= 44>, Top: 17( 1 )[ 1 : 17 : 0.800 ]( 17 , 18 , 143 ),<|>Tot Used: 316 , Added: 18 , Zero Std: 0 , Max Cor: 0.988
#>
12 <R=0.988,w= 4,N= 44>, Top: 2( 1 )[ 1 : 2 : 0.844 ]( 2 , 2 , 150 ),<|>Tot Used: 316 , Added: 2 , Zero Std: 0 , Max Cor: 0.994
#>
13 <R=0.994,w= 5,N= 4>, Top: 2( 1 )[ 1 : 2 : 0.800 ]( 2 , 2 , 151 ),<|>Tot Used: 316 , Added: 2 , Zero Std: 0 , Max Cor: 0.951
#>
14 <R=0.951,w= 6,N= 2>, Top: 1( 1 )[ 1 : 1 : 0.800 ]( 1 , 1 , 152 ),<|>Tot Used: 316 , Added: 1 , Zero Std: 0 , Max Cor: 0.799
#>
15 <R=0.000,w= 7,N= 0>
#>
[ 15 ], 0.7986104 Decor Dimension: 316 . Cor to Base: 196 , ABase: 33 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
616
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
164
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
4.84
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
3.39
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPSTM <- attr(DEdataframe,"UPSTM")
gplots::heatmap.2(1.0*(abs(UPSTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.997621
classes <- unique(dataframe[,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[,outcome],col=raincolors[dataframe[,outcome]+1])
datasetframe.umap = umap(scale(DEdataframe[,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[,outcome],col=raincolors[DEdataframe[,outcome]+1])
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
100 : ECG_p_LF_mean 200 : IT_CCV_LF 300 : EDA_Original_mad_D
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
100 : La_ECG_p_LF_mean 200 : La_IT_CCV_LF 300 : La_EDA_Original_mad_D
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##topfive
topvar <- c(1:length(varlist)) <= TopVariables
pander::pander(univarRAW$orderframe[topvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| ECG_hrv_prctile75 | 0.483 | 1.031 | -0.1471 | 1.286 | 0.0849 | 0.731 |
| ECG_hrv_geomean_A_ | -0.105 | 1.208 | 0.5327 | 1.093 | 0.1001 | 0.727 |
| IT_LF_baseline_D | 0.518 | 1.044 | -0.2219 | 0.700 | 0.5845 | 0.721 |
| IT_p_Total_baseline | 0.507 | 1.009 | -0.2168 | 0.668 | 0.6797 | 0.721 |
| IT_VLF_baseline | 0.492 | 0.990 | -0.2217 | 0.652 | 0.7285 | 0.720 |
| ECG_hrv_prctile25 | 0.244 | 1.150 | -0.4451 | 0.938 | 0.5416 | 0.719 |
| IT_PSD_baseline | 0.554 | 1.095 | -0.1829 | 0.773 | 0.2875 | 0.715 |
| ECG_hrv_mean | 0.259 | 0.865 | -0.3517 | 0.931 | 0.2559 | 0.714 |
| IT_HF_baseline | 0.692 | 1.254 | -0.0466 | 1.052 | 0.0555 | 0.713 |
| ECG_hrv_trimmean25 | 0.282 | 0.950 | -0.3372 | 0.967 | 0.5083 | 0.711 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
theLaVar <- rownames(finalTable)[str_detect(rownames(finalTable),"La_")]
pander::pander(univarDe$orderframe[topLAvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| La_EDA_Original_baseline_D | -0.17678 | 0.3481 | 0.1236 | 0.3119 | 0.16752 | 0.772 |
| La_IT_BRV_baseline | -0.14953 | 0.4231 | 0.2053 | 0.2854 | 0.96581 | 0.762 |
| La_EDA_Original_prctile75_D | -0.00243 | 0.0214 | 0.0136 | 0.0208 | 0.00621 | 0.754 |
| La_ECG_RR_window_baseline | 0.13890 | 0.2973 | -0.0886 | 0.2857 | 0.79161 | 0.747 |
| La_EDA_Original_mad_D | -0.01395 | 0.0530 | 0.0239 | 0.0323 | 0.39741 | 0.740 |
| La_EDA_processed_std_D | 0.24655 | 0.3816 | -0.0878 | 0.3303 | 0.17461 | 0.724 |
| IT_VLF_baseline | 0.49163 | 0.9899 | -0.2217 | 0.6524 | 0.72855 | 0.720 |
| ECG_hrv_mean | 0.25862 | 0.8655 | -0.3517 | 0.9306 | 0.25586 | 0.714 |
| La_EDA_processed_prctile25_D | -0.25362 | 0.5637 | 0.1721 | 0.4719 | 0.39964 | 0.699 |
| La_EDA_processed_median_D | -0.29650 | 0.6597 | 0.0421 | 0.2853 | 0.13809 | 0.694 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
theSigDc <- dc[theLaVar]
names(theSigDc) <- NULL
theSigDc <- unique(names(unlist(theSigDc)))
theFormulas <- dc[rownames(finalTable)]
deFromula <- character(length(theFormulas))
names(deFromula) <- rownames(finalTable)
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 2.79 | 277 | 0.759 |
allSigvars <- names(dc)
dx <- names(deFromula)[1]
for (dx in names(deFromula))
{
coef <- theFormulas[[dx]]
cname <- names(theFormulas[[dx]])
names(cname) <- cname
for (cf in names(coef))
{
if (cf != dx)
{
if (coef[cf]>0)
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("+ %5.3f*%s",coef[cf],cname[cf]))
}
else
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("%5.3f*%s",coef[cf],cname[cf]))
}
}
}
}
finalTable <- rbind(finalTable,univarRAW$orderframe[theSigDc[!(theSigDc %in% rownames(finalTable))],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- deFromula[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| La_EDA_Original_baseline_D | -1.053EDA_Original_prctile25_D + 1.000EDA_Original_baseline_D | -0.17678 | 0.3481 | 0.12363 | 0.3119 | 0.16752 | 0.772 | 0.496 | 0 |
| La_IT_BRV_baseline | -0.833IT_BRV_mean + 1.000IT_BRV_baseline | -0.14953 | 0.4231 | 0.20534 | 0.2854 | 0.96581 | 0.762 | 0.642 | -1 |
| La_EDA_Original_prctile75_D | -0.456EDA_Original_max_D -0.544EDA_Original_prctile25_D + 1.000*EDA_Original_prctile75_D | -0.00243 | 0.0214 | 0.01360 | 0.0208 | 0.00621 | 0.754 | 0.570 | 5 |
| La_ECG_RR_window_baseline | -0.892ECG_RR_window_trimmean25 + 1.000ECG_RR_window_baseline | 0.13890 | 0.2973 | -0.08862 | 0.2857 | 0.79161 | 0.747 | 0.522 | 0 |
| La_EDA_Original_mad_D | -1.026EDA_Original_std_D + 1.000EDA_Original_mad_D | -0.01395 | 0.0530 | 0.02389 | 0.0323 | 0.39741 | 0.740 | 0.507 | 0 |
| La_EDA_processed_std_D | -0.890EDA_Original_std_D + 1.000EDA_processed_std_D | 0.24655 | 0.3816 | -0.08778 | 0.3303 | 0.17461 | 0.724 | 0.581 | 2 |
| IT_VLF_baseline | 0.49163 | 0.9899 | -0.22167 | 0.6524 | 0.72855 | 0.720 | 0.720 | 5 | |
| ECG_hrv_mean | 0.25862 | 0.8655 | -0.35169 | 0.9306 | 0.25586 | 0.714 | 0.714 | 4 | |
| La_EDA_processed_prctile25_D | + 0.996EDA_Original_std_D + 1.000EDA_processed_prctile25_D | -0.25362 | 0.5637 | 0.17213 | 0.4719 | 0.39964 | 0.699 | 0.588 | 0 |
| La_EDA_processed_median_D | -0.906EDA_processed_trimmean25_D + 1.000EDA_processed_median_D | -0.29650 | 0.6597 | 0.04206 | 0.2853 | 0.13809 | 0.694 | 0.530 | -1 |
| IT_BRV_baseline | NA | -0.35971 | 0.8468 | -0.00106 | 1.0856 | 0.08274 | 0.642 | 0.642 | NA |
| ECG_RR_window_trimmean25 | NA | -0.08973 | 0.9854 | 0.23086 | 0.9766 | 0.96455 | 0.606 | 0.606 | 12 |
| EDA_processed_prctile25_D | NA | -0.65968 | 1.3545 | -0.25260 | 1.0934 | 0.02142 | 0.588 | 0.588 | NA |
| EDA_processed_trimmean25_D | NA | -0.39525 | 1.3811 | -0.61752 | 1.3648 | 0.01157 | 0.582 | 0.582 | 2 |
| EDA_processed_std_D | NA | 0.60948 | 1.1881 | 0.29184 | 0.9118 | 0.01488 | 0.581 | 0.581 | NA |
| EDA_Original_max_D | NA | 0.63696 | 1.3434 | 0.36718 | 1.1892 | 0.00726 | 0.575 | 0.575 | NA |
| EDA_Original_prctile25_D | NA | 0.66082 | 1.4080 | 0.37549 | 1.2295 | 0.00966 | 0.573 | 0.573 | 34 |
| EDA_Original_prctile75_D | NA | 0.64742 | 1.3754 | 0.38526 | 1.2143 | 0.01041 | 0.570 | 0.570 | NA |
| EDA_processed_median_D | NA | -0.65445 | 1.3990 | -0.51718 | 1.3120 | 0.00378 | 0.530 | 0.530 | NA |
| ECG_RR_window_baseline | NA | 0.05886 | 0.9529 | 0.11732 | 0.9276 | 0.77168 | 0.522 | 0.522 | NA |
| EDA_Original_mad_D | NA | 0.40428 | 1.0948 | 0.46135 | 1.1988 | 0.01686 | 0.507 | 0.507 | NA |
| IT_BRV_mean | NA | -0.25243 | 1.0855 | -0.24788 | 1.0873 | 0.27583 | 0.503 | 0.503 | 5 |
| EDA_Original_baseline_D | NA | 0.51887 | 1.4468 | 0.51891 | 1.4457 | 0.00876 | 0.496 | 0.496 | NA |
| EDA_Original_std_D | NA | 0.40771 | 1.0812 | 0.42646 | 1.1545 | 0.02035 | 0.490 | 0.490 | 10 |